[go: up one dir, main page]

CN118813903B - Big data-based electric furnace steelmaking carbon content control system - Google Patents

Big data-based electric furnace steelmaking carbon content control system Download PDF

Info

Publication number
CN118813903B
CN118813903B CN202411300972.2A CN202411300972A CN118813903B CN 118813903 B CN118813903 B CN 118813903B CN 202411300972 A CN202411300972 A CN 202411300972A CN 118813903 B CN118813903 B CN 118813903B
Authority
CN
China
Prior art keywords
adjustment
carbon content
carbon
data
electric furnace
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202411300972.2A
Other languages
Chinese (zh)
Other versions
CN118813903A (en
Inventor
陈献
刘小明
陈辉
刘峰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sichuan Derun Iron And Steel Group Hangda Iron And Steel Co ltd
Original Assignee
Sichuan Derun Iron And Steel Group Hangda Iron And Steel Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Sichuan Derun Iron And Steel Group Hangda Iron And Steel Co ltd filed Critical Sichuan Derun Iron And Steel Group Hangda Iron And Steel Co ltd
Priority to CN202411300972.2A priority Critical patent/CN118813903B/en
Publication of CN118813903A publication Critical patent/CN118813903A/en
Application granted granted Critical
Publication of CN118813903B publication Critical patent/CN118813903B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/20—Identification of molecular entities, parts thereof or of chemical compositions
    • C—CHEMISTRY; METALLURGY
    • C21—METALLURGY OF IRON
    • C21C—PROCESSING OF PIG-IRON, e.g. REFINING, MANUFACTURE OF WROUGHT-IRON OR STEEL; TREATMENT IN MOLTEN STATE OF FERROUS ALLOYS
    • C21C5/00—Manufacture of carbon-steel, e.g. plain mild steel, medium carbon steel or cast steel or stainless steel
    • C21C5/52—Manufacture of steel in electric furnaces
    • C—CHEMISTRY; METALLURGY
    • C21—METALLURGY OF IRON
    • C21C—PROCESSING OF PIG-IRON, e.g. REFINING, MANUFACTURE OF WROUGHT-IRON OR STEEL; TREATMENT IN MOLTEN STATE OF FERROUS ALLOYS
    • C21C5/00—Manufacture of carbon-steel, e.g. plain mild steel, medium carbon steel or cast steel or stainless steel
    • C21C5/52—Manufacture of steel in electric furnaces
    • C21C5/527—Charging of the electric furnace
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F27—FURNACES; KILNS; OVENS; RETORTS
    • F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
    • F27D19/00—Arrangements of controlling devices
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/10—Analysis or design of chemical reactions, syntheses or processes
    • C—CHEMISTRY; METALLURGY
    • C21—METALLURGY OF IRON
    • C21C—PROCESSING OF PIG-IRON, e.g. REFINING, MANUFACTURE OF WROUGHT-IRON OR STEEL; TREATMENT IN MOLTEN STATE OF FERROUS ALLOYS
    • C21C5/00—Manufacture of carbon-steel, e.g. plain mild steel, medium carbon steel or cast steel or stainless steel
    • C21C5/52—Manufacture of steel in electric furnaces
    • C21C2005/5288—Measuring or sampling devices
    • C—CHEMISTRY; METALLURGY
    • C21—METALLURGY OF IRON
    • C21C—PROCESSING OF PIG-IRON, e.g. REFINING, MANUFACTURE OF WROUGHT-IRON OR STEEL; TREATMENT IN MOLTEN STATE OF FERROUS ALLOYS
    • C21C2300/00—Process aspects
    • C21C2300/06—Modeling of the process, e.g. for control purposes; CII
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F27—FURNACES; KILNS; OVENS; RETORTS
    • F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
    • F27D19/00—Arrangements of controlling devices
    • F27D2019/0003—Monitoring the temperature or a characteristic of the charge and using it as a controlling value
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F27—FURNACES; KILNS; OVENS; RETORTS
    • F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
    • F27D19/00—Arrangements of controlling devices
    • F27D2019/0028—Regulation
    • F27D2019/0075—Regulation of the charge quantity

Landscapes

  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Materials Engineering (AREA)
  • Metallurgy (AREA)
  • Organic Chemistry (AREA)
  • Computing Systems (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Manufacturing & Machinery (AREA)
  • Mechanical Engineering (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Analytical Chemistry (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Waste-Gas Treatment And Other Accessory Devices For Furnaces (AREA)

Abstract

The invention relates to the technical field of process control, in particular to a large-data-based electric furnace steelmaking carbon content control system which comprises a carbon content preliminary control module, a carbon content dynamic adjustment module, a carbon content monitoring and feedback module and an electric furnace efficiency optimization module. According to the invention, the predictability and consistency of the steelmaking process are improved by precisely controlling the initial carbon content, the furnace temperature and the material speed, the fluctuation of the product quality is obviously reduced, the generation and application of real-time adjustment data enable the furnace temperature control and the material speed to rapidly respond to the change of the carbon content, the energy consumption is reduced, the production efficiency is improved, the energy use is optimized by a real-time feedback mechanism, the product quality is improved, the carbon content stability analysis provides a fine regulation and control basis by continuously monitoring the deviation of the carbon content and an expected value, the human error is reduced, the system reaction speed and accuracy are improved, the control flow not only reduces the carbon emission, but also enhances the market competitiveness of steelmaking enterprises, and the economic and environmental benefits are brought.

Description

Big data-based electric furnace steelmaking carbon content control system
Technical Field
The invention relates to the technical field of process control, in particular to a large-data-based electric furnace steelmaking carbon content control system.
Background
The technical field of process control is a technology applied to industrial production and manufacturing processes, and aims to ensure product quality, improve production efficiency and minimize cost and energy consumption by monitoring and adjusting various parameters in the production process. In process control, the collection, analysis and feedback of data allows the process to be automatically adjusted to achieve a preset performance goal. Process control is widely used in many industries including chemical, pharmaceutical, food processing, and metal smelting, including industries where strict requirements are placed on product quality, safety, and environmental impact.
The system is mainly used for precisely controlling the carbon content of molten steel in the steelmaking process, has direct influence on the mechanical properties of steel such as hardness, strength, plasticity and the like, and can effectively ensure the quality of products, optimize energy consumption and reduce carbon emission in the production process by monitoring and adjusting the carbon content in the furnace in real time. Under the background of low carbon economy, the control system for the carbon content in electric furnace steelmaking is particularly important, not only improves the economic benefit of steelmaking, but also meets the requirements of environmental protection and sustainable development, and the application of the system is beneficial to better managing the carbon quota of steelmaking enterprises in the carbon trade market, thereby reducing the operation cost and improving the market competitiveness.
Although the existing electric furnace steelmaking carbon content control system is widely applied to the steelmaking field, common problems in actual operation include insufficient accuracy of setting initial operation conditions and insufficient rapid real-time adjustment reaction of production processes, the technology depends on the traditional sensor and control system, the data processing and reaction time is insufficient for processing the production processes with high variability, such as electric furnace steelmaking, and the prior art cannot realize optimization in the aspect of carbon content control, so that energy waste and production efficiency are not ideal. For example, the lack of real-time data analysis results in a lag in the adjustment of the carbon content, thereby affecting the mechanical properties of the steel and the quality of the product. The lack of highly integrated and automated systems also increases reliance on operators, increases the risk of errors during operation, makes it difficult for businesses to remain competitive today for high efficiency and low carbon emissions, and increases economic losses due to product failure.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a large-data-based electric furnace steelmaking carbon content control system.
In order to achieve the aim, the invention adopts the following technical scheme that the electric furnace steelmaking carbon content control system based on big data comprises:
The carbon content preliminary control module sets initial conditions of electric furnace operation, including temperature, time, charging speed and initial carbon content, based on historical operation data and standard production requirements, acquires initial operation parameters, and adjusts furnace temperature control and charging speed of the electric furnace according to the initial operation parameters to form a preliminary carbon control adjustment strategy;
the carbon content dynamic adjustment module carries out real-time adjustment on furnace temperature control and material speed based on the preliminary carbon control adjustment strategy to obtain real-time adjustment data, and calculates the expected change of the adjusted carbon content according to the real-time adjustment data to generate an expected carbon content adjustment result;
The carbon content monitoring and feedback module monitors deviation of the real-time carbon content and the expected carbon content by utilizing the expected carbon content adjusting result to generate a carbon content stability analysis result, and adjusts the operation parameters of the electric furnace and optimizes carbon content monitoring to generate a carbon content parameter optimization scheme based on the carbon content stability analysis result;
and the electric furnace efficiency optimization module adjusts the operation parameters of the electric furnace according to the carbon content parameter optimization scheme, matches a preset carbon content level, evaluates the influence of parameter adjustment, compares the energy consumption data before and after adjustment, records the change of key production indexes and generates a carbon control efficiency analysis result.
As a further aspect of the present invention, the step of obtaining the initial operation parameter specifically includes:
Based on the historical operation data and standard production requirements, key operation parameters including temperature, time, charging speed and initial carbon content are extracted from the electric furnace operation data and integrated into a historical operation data set, the historical operation data set is compared with the standard production parameters of the electric furnace, and the formula is adopted: Calculating a percentage of deviation, generating a set of deviation data, wherein, Represents the first of the standard parametersThe parameters of the parameters are set to be,Representing the first of the historical operating dataThe parameters of the parameters are set to be,Representing the number of parameters to be used,Represents the average deviation percentage of the parameter;
dynamically adjusting standard production parameters of the electric furnace by utilizing the information in the deviation data set, and applying the formula: and calculating and obtaining an adjusted set of operating parameters, wherein, Representing the standard parameters that are not to be adjusted,Representing the parameters after the adjustment of the parameters,Representing the adjustment factor based on the deviation,Represents the average deviation percentage of the parameter;
Combining the adjusted set of operating parameters with the current production requirements and equipment status, applying the formula: an initial operating parameter is generated, wherein, Representing the initial operating parameters of the vehicle,AndIs an adjustment factor that is used to adjust the position of the device,For the regularization constant,Representing the adjusted parameters.
As a further scheme of the present invention, the preliminary carbon control adjustment strategy obtaining step specifically includes:
Based on the initial operation parameters, capturing the original furnace temperature and the material speed, and adopting a formula according to the material reaction coefficient and the adjustment coefficient: The current carbon concentration result is calculated and obtained, wherein, Representing the current furnace temperature of the furnace,Representing the material speed, the material speed is represented,Is the reaction coefficient of the material, and the reaction coefficient of the material,In order to adjust the coefficient of the power supply,Is the current carbon concentration;
Comparing the current carbon concentration result with the target carbon control level, calculating the adjustment demand percentage, and adopting a formula Generating a carbon control adjustment requirement, wherein,Representing the level of carbon control of the target,As the current carbon concentration is to be determined,In order to adjust the percentage of demand,Is a correction factor;
according to the carbon control adjustment requirement, adjusting the furnace temperature and the material speed, and adopting the formula: And Generating a preliminary carbon control adjustment strategy, wherein,For the setting of the new furnace temperature,For the setting of the new material speed,Representing the current furnace temperature of the furnace,Representing the material speed, the material speed is represented,To adjust the percentage of demand.
As a further aspect of the present invention, the step of acquiring the real-time adjustment data specifically includes:
Based on the preliminary carbon control adjustment strategy, the temperature in the furnace is measured in real time, temperature data is converted into control input through temperature sensor recording, and the formula is adopted: generating a temperature control parameter, wherein, Representing the current measured furnace temperature,For the temperature adjustment coefficient, the temperature of the material is adjusted,In order for the attenuation factor to be a factor,For the maximum sustainable temperature of the device,Representing a temperature control parameter;
according to the temperature control parameters, the material speed is adjusted by adopting the formula: Obtaining a material speed control parameter, wherein, Representing the speed of the reference material,Representing the set temperature of the product,Representing the adjustment factor of the material speed,Representing the control parameter of the material speed,Representing a temperature control parameter;
The temperature control parameters and the material speed control parameters are combined, the current temperature and material speed information in the furnace is collected, data are transmitted to the control unit, the average value and the deviation of the temperature data and the material speed data are analyzed, the temperature and the material speed setting of the electric furnace are adjusted, and the formula is adopted: Acquiring real-time adjustment data, wherein, Representing the control parameter of the material speed,Representing the temperature control parameter of the temperature of the liquid,Representing real-time adjustment data.
As a further aspect of the present invention, the step of obtaining the expected carbon content adjustment result specifically includes:
Based on the real-time adjustment data, real-time carbon content data is collected at regular time through an environment monitoring sensor, noise and abnormal values are removed by utilizing a data cleaning algorithm, and the method is as follows: a real-time carbon data set is obtained, wherein, Representing the carbon content of each data point,Representing the number of points of the total data,Representing the average value of the data points,Representing the processed dataset;
based on the real-time carbon dataset and the environmental change factor, the formula is used: calculating and obtaining the expected carbon content variation, wherein, Representing the coefficient of influence of the environmental change,Representing a set of data that has been processed,Represents the expected amount of carbon content change;
according to the expected carbon content variation and the environmental threshold set by the policy, the formula is utilized: The result of the desired carbon content adjustment is obtained, wherein, Representing the amount of variation in the carbon content expected,Representing an environmental policy threshold value,Representing the result of the adjusted carbon content,Representing the processed dataset.
As a further aspect of the present invention, the step of obtaining the carbon content stability analysis result specifically includes:
based on the expected carbon content adjustment result, calculating the deviation of the real-time carbon content obtained by environmental monitoring and the expected carbon content adjustment result by comparing the two, and adopting the formula: a deviation from the expected carbon content in real time is generated, wherein, Representing the carbon content monitored in real-time,Indicating the result of the desired carbon content adjustment,Indicating a deviation in real time from the expected carbon content,Is a stability adjustment parameter;
Comparing the deviation between the real-time and expected carbon content with a set stability threshold to determine whether adjustment is needed, and using the formula: an adjustment demand flag is generated, wherein, For a deviation from the expected carbon content in real time,As a threshold value for the stability,To adjust the demand flag (if the result is greater than or equal to 1, then adjustment is required, otherwise not required),Is a weight parameter;
According to the adjustment requirement mark and the deviation between the real-time and the expected carbon content, and combining the requirement of stability analysis, adopting the formula: Generating a carbon content stability analysis result, wherein, In order to adjust the demand sign(s),For a deviation from the expected carbon content in real time,As a result of the analysis of the stability of the carbon content,Is a normalization parameter.
As a further aspect of the present invention, the step of obtaining the carbon content parameter optimization scheme specifically includes:
and calculating the carbon emission deviation by using the analysis result of the carbon content stability and combining the key adjustment coefficient of the electric furnace, and adopting the formula: a real-time carbon offset is generated, wherein, The results of the carbon content stability analysis are shown,For the adjustment of the coefficient of carbon deviation,Is the carbon deviation amount;
based on the real-time carbon deviation, determining an operation parameter adjustment amount of the electric furnace by adopting feedback adjustment, and adopting the formula: Introducing an electric furnace performance adjustment factor and an adjustment baseline coefficient to generate an electric furnace operation parameter adjustment quantity, wherein, Is the amount of carbon deviation that is used,AndThe performance adjustment factor and the adjustment baseline coefficient are respectively,The adjustment quantity of the operation parameters of the electric furnace;
According to the electric furnace operation parameter adjustment quantity, optimizing electric furnace parameters and reducing carbon emission by an optimization algorithm, and using the formula: Generating a carbon content parameter optimization scheme, wherein, For the adjustment of the operating parameters of the electric furnace,In order to optimize the coefficient of efficiency,And optimizing the scheme for the carbon content parameter.
As a further aspect of the present invention, the step of obtaining the analysis result of the carbon control efficiency specifically includes:
based on the carbon content parameter optimization scheme and the current operation parameters of the electric furnace, adjusting the parameters and matching the preset carbon content level, and adopting the formula: generating updated operating parameters of the electric furnace, wherein, Indicating the original operating parameters of the electric furnace,Is the coefficient of optimization and is used to determine,AndIs a new coefficient of the influence of the adjustment,The updated electric furnace operation parameters;
and evaluating the influence on the carbon emission and the energy consumption by using the updated electric furnace operation parameters, and adopting the formula: Generating adjusted energy consumption data, wherein, As the raw energy consumption data is to be used,In order to update the operating parameters of the electric furnace,AndIn order to adjust the coefficient of energy consumption,The energy consumption data after the adjustment;
according to the adjusted energy consumption data and the production index, adopting the formula: Calculating and generating a carbon control efficiency analysis result, wherein, AndRespectively the energy consumption data before and after adjustment,In order to produce an index of the efficiency of production,And represents the analysis result of the carbon control efficiency.
Compared with the prior art, the invention has the advantages and positive effects that:
In the invention, the predictability and consistency of the steelmaking process are obviously improved by precisely controlling the initial carbon content, the furnace temperature and the material speed, so that the fluctuation of the product quality is reduced, the generation and the use of data are regulated in real time, the furnace temperature control and the material speed regulation can immediately respond to the expected change of the carbon content, the energy consumption is further reduced, the production efficiency is improved, the real-time feedback mechanism not only ensures the product quality, but also optimizes the energy use, because the implementation of the carbon content stability analysis can be quickly regulated even if the tiny carbon content fluctuation, finer regulation basis is provided for the operation of the electric furnace by continuously monitoring the deviation of the carbon content from the expected value, the continuity and the automation of the monitoring reduce the possibility of human errors, the integral reaction speed and the accuracy of the system are improved, and the fine control flow not only reduces the carbon emission in the production, but also strengthens the competitive power of steel-making enterprises in the carbon trade market by optimizing the operation parameters, thereby bringing economic and environmental protection benefits to enterprises.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a flow chart of initial operating parameters in the present invention;
FIG. 3 is a flow chart of a preliminary carbon control adjustment strategy according to the present invention;
FIG. 4 is a flow chart of the real-time adjustment of data according to the present invention;
FIG. 5 is a flow chart of the result of the contemplated carbon content adjustment in the present invention;
FIG. 6 is a flow chart of the results of carbon content stability analysis in accordance with the present invention;
FIG. 7 is a flow chart of a carbon content parameter optimization scheme in the present invention;
FIG. 8 is a flow chart of the analysis results of the carbon control efficiency in the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Referring to fig. 1, a system for controlling carbon content in electric furnace steelmaking based on big data comprises:
the initial control module of the carbon content sets initial conditions of the electric furnace operation based on historical operation data and standard production requirements, including temperature, time, charging speed and initial carbon content, acquires initial operation parameters, and adjusts furnace temperature control and charging speed of the electric furnace according to the initial operation parameters to form an initial carbon control adjustment strategy;
The carbon content dynamic adjustment module carries out real-time adjustment on furnace temperature control and material speed based on a preliminary carbon control adjustment strategy, acquires real-time adjustment data, calculates the expected change of the adjusted carbon content through the real-time adjustment data, and generates an expected carbon content adjustment result;
The carbon content monitoring and feedback module monitors deviation of real-time carbon content and expected carbon content by using an expected carbon content adjusting result to generate a carbon content stability analysis result, adjusts operation parameters of the electric furnace and optimizes carbon content monitoring based on the carbon content stability analysis result to generate a carbon content parameter optimization scheme;
The electric furnace efficiency optimization module adjusts the operation parameters of the electric furnace according to the carbon content parameter optimization scheme, matches the preset carbon content level, evaluates the influence of parameter adjustment, compares the energy consumption data before and after adjustment, records the change of key production indexes and generates a carbon control efficiency analysis result.
The preliminary carbon control adjustment strategy comprises a furnace temperature adjustment target, a material adjustment target and a carbon balance preliminary setting, the real-time adjustment data comprises a temperature monitoring value, a speed monitoring value and a carbon content detection value, the expected carbon content adjustment result comprises a target carbon content, a predicted carbon deviation and an adjustment amplitude, the carbon content stability analysis result comprises a deviation threshold value, stability classification and an adjustment strategy, the carbon content parameter optimization scheme comprises an adjusted furnace temperature standard, an adjusted material speed standard and a carbon content adjustment index, and the carbon control efficiency analysis result comprises energy consumption efficiency, production efficiency and carbon emission data.
Referring to fig. 2, the initial operation parameters are obtained by the steps of:
Based on the historical operation data and standard production requirements, key operation parameters including temperature, time, charging speed and initial carbon content are extracted from the electric furnace operation data and integrated into a historical operation data set, and the historical operation data set and the standard production parameters of the electric furnace are combined by adopting the formula: Calculating a percentage of deviation, generating a set of deviation data, wherein, Represents the first of the standard parametersThe parameters of the parameters are set to be,Representing the first of the historical operating dataThe parameters of the parameters are set to be,Representing the number of parameters to be used,Represents the average deviation percentage of the parameter;
dynamically adjusting standard production parameters of the electric furnace by utilizing information in the deviation data set, and applying the formula: and calculating and obtaining an adjusted set of operating parameters, wherein, Representing the standard parameters that are not to be adjusted,Representing the parameters after the adjustment of the parameters,Representing the adjustment factor based on the deviation,Represents the average deviation percentage of the parameter;
Combining the adjusted set of operating parameters with the current production requirements and equipment status, applying the formula: an initial operating parameter is generated, wherein, Representing the initial operating parameters of the vehicle,AndIs an adjustment factor that is used to adjust the position of the device,For the regularization constant,Representing the adjusted parameters.
Average deviation percentage formula of parameters: Assume 3 parameters (temperature, time, feed rate), standard parameters And actual measured valuesThe following are provided:
Temperature: The degree of the heat dissipation, A degree;
Time: the time required for the reaction is one minute, Minutes;
Feed rate: kg/h,kg/h;
calculating the deviation percentage of the temperature: (or 3.33%);
calculating the deviation percentage of time: (or 11.11%);
calculating the deviation percentage of the feeding speed: (or 5%);
Percentage of overall average deviation: . This value is Represents the average deviation percentage, reflecting the average deviation degree of the operating parameter from the standard parameter.
The adjusted parameter formula: let us assume that the intensity coefficient is adjusted Using the calculated in the previous stepCalculating(For temperature adjustment): . The same calculations apply to time and feed rate, this value Representing the adjusted parameter values for approaching actual operating conditions.
Initial operating parameter formula: Assume that the parameters are: ,,, (temperature); calculation :;;. This value isRepresenting initial operating parameters adjusted for current production needs and plant conditions.
Referring to fig. 3, the preliminary carbon control adjustment strategy is obtained by the following steps:
Based on initial operation parameters, capturing the original furnace temperature and the material speed, and adopting the formula according to the material reaction coefficient and the adjustment coefficient: The current carbon concentration result is calculated and obtained, wherein, Representing the current furnace temperature of the furnace,Representing the material speed, the material speed is represented,Is the reaction coefficient of the material, and the reaction coefficient of the material,In order to adjust the coefficient of the power supply,Is the current carbon concentration;
Comparing the current carbon concentration result with the target carbon control level, calculating the adjustment demand percentage, and adopting a formula Generating a carbon control adjustment requirement, wherein,Representing the level of carbon control of the target,As the current carbon concentration is to be determined,In order to adjust the percentage of demand,Is a correction factor;
according to the carbon control adjustment requirement, adjusting the furnace temperature and the material speed, and adopting the formula: And Generating a preliminary carbon control adjustment strategy, wherein,For the setting of the new furnace temperature,For the setting of the new material speed,Representing the current furnace temperature of the furnace,Representing the material speed, the material speed is represented,To adjust the percentage of demand.
Current carbon concentration formula:; The furnace temperature is assumed to be 1500 ℃, and the operation temperature of the electric furnace is shown; (feed rate) assuming 200kg/h, representing the amount of material passing through the furnace per hour; the (adjustment coefficient) is a coefficient set according to the thermal sensitivity of the material, here assumed to be 0.5, indicating the sensitivity of the material reaction to temperature changes; (material reaction coefficient) assuming 0.8, representing the chemical reactivity of the material;
firstly, calculating the ratio of temperature to material speed to obtain Raising the result of the ratio to the power of 0.5 to obtainMultiplying by material reaction coefficientObtainingAnd then the obtainedThis value represents the current carbon concentration, i.e. the amount of carbon produced by the reaction of the material per unit time at a given furnace temperature and feed rate.
And (3) adjusting a demand percentage formula:; (target carbon control level) is set to 0.5, indicating the desired carbon concentration; (current carbon concentration) 0.292 calculated from the previous step; the correction factor is 1.1, and is used for regulating the calculated result to better adapt to the actual operation condition, calculating the difference ratio of target carbon concentration and current carbon concentration to obtain Raising the result of the difference ratio to the power of 0.5 to obtainMultiplying by a correction factorObtainingAnd then the obtainedThis indicates that the current carbon concentration needs to be increased by 92.8% to reach the target carbon control level.
Preliminary carbon control adjustment strategy formula: And ;AndFurnace temperature and material speed; The adjustment demand percentage obtained from the previous step; according to the adjustment demand percentage, calculating a new furnace temperature: . Calculating a new material speed: . New furnace temperature About 1514 degrees, new material speedAbout 198kg/h, adjusted to achieve the target carbon control level.
Referring to fig. 4, the steps of acquiring the real-time adjustment data specifically include:
Based on a preliminary carbon control adjustment strategy, measuring the temperature in the furnace in real time, recording the temperature data into control input through a temperature sensor, and adopting the formula: generating a temperature control parameter, wherein, Representing the current measured furnace temperature,For the temperature adjustment coefficient, the temperature of the material is adjusted,In order for the attenuation factor to be a factor,For the maximum sustainable temperature of the device,Representing a temperature control parameter;
according to the temperature control parameters, regulating the material speed, and adopting the formula: Obtaining a material speed control parameter, wherein, Representing the speed of the reference material,Representing the set temperature of the product,Representing the adjustment factor of the material speed,Representing the control parameter of the material speed,Representing a temperature control parameter;
The temperature control parameters and the material speed control parameters are combined, the current temperature and material speed information in the furnace is collected, data are transmitted to the control unit, the average value and the deviation of the temperature data and the material speed data are analyzed, the temperature and the material speed setting of the electric furnace are adjusted, and the formula is adopted: Acquiring real-time adjustment data, wherein, Representing the control parameter of the material speed,Representing the temperature control parameter of the temperature of the liquid,Representing real-time adjustment data.
The current measured furnace temperature formula:; the actual measured furnace temperature is assumed to be 800 ℃; Setting the temperature adjustment coefficient to 0.05 to indicate that the temperature is adjusted to 105% of the original temperature; The decay factor associated with the maximum temperature, assumed to be 30; the maximum bearable temperature of the equipment is assumed to be 1200 ℃;
Calculating the furnace temperature under the influence of the adjustment coefficient: calculating an attenuation term: final temperature control parameters: . This calculation shows that the temperature control parameter is set to about 815.52 degrees taking into account the maximum temperature of the plant and the actual furnace temperature.
The formula of the material speed control parameter is as follows:; The reference material speed is assumed to be 500m/min; setting the temperature to be 850 ℃; a material speed adjustment factor is assumed to be 0.1; the temperature control parameters obtained in the previous step are about 815.52 ℃, and the proportion of the temperature difference is calculated: adjusting the influence of the feed rate factor: final material speed control parameters: . This calculation shows a slight decrease in the material speed, adjusted to about 497.98m/min, to accommodate the difference between the set temperature and the actual control temperature.
Adjusting a data formula in real time:; the material speed control parameter is about 497.98m/min; the temperature control parameters are about 815.52 ℃, and the parameter average value is calculated: Calculating the deviation ratio: And finally, adjusting data in real time: . This calculation shows that the real-time adjustment data is 656.508, which is an adjustment value that combines the speed and temperature.
Referring to fig. 5, the steps for obtaining the expected carbon content adjustment result are specifically as follows:
Based on the real-time adjustment data, the real-time carbon content data is collected at regular time through the environment monitoring sensor, noise and abnormal values are removed by utilizing a data cleaning algorithm, and the method is as follows: a real-time carbon data set is obtained, wherein, Representing the carbon content of each data point,Representing the number of points of the total data,Representing the average value of the data points,Representing the processed dataset;
Based on the real-time carbon dataset and the environmental change factor, the formula is used: calculating and obtaining the expected carbon content variation, wherein, Representing the coefficient of influence of the environmental change,Representing a set of data that has been processed,Represents the expected amount of carbon content change;
Environmental thresholds set according to the expected carbon content variation and policy, using the formula: The result of the desired carbon content adjustment is obtained, wherein, Representing the amount of variation in the carbon content expected,Representing an environmental policy threshold value,Representing the result of the adjusted carbon content,Representing the processed dataset.
The processed dataset formula:; Carbon content of the i-th data point; Average value of all data points; Total number of data points; the processed dataset assumed that there were 5 data points of carbon content respectively Ppm, first calculate the average of the data points:. The average value was subtracted from the carbon content of each data point and summed: Dividing by the total number of data points To obtain:. This result shows that after the average adjustment, the average change of the data is zero, showing that the dataset has been centered.
Expected carbon content variation formula:; environmental change influence coefficients; Representing the processed data set; Expected carbon content variation, assuming environmental change coefficients Obtained from the previous stepSubstituting the value into the formula: This result shows that the expected change in carbon content is zero after the environmental change is small and the data is centered.
The adjusted carbon content results formula:; Representing the processed data set; expected carbon content variation; Environmental policy thresholds; the adjusted carbon content result is assumed to be the environmental policy threshold value Ppm from the previous stepAndSubstituting the formula: . This result indicates that there is no carbon content to be adjusted under the current environmental policy, with the final adjustment result being zero.
Referring to fig. 6, the steps for obtaining the analysis result of carbon content stability are specifically:
based on the expected carbon content adjustment result, the deviation of the real-time carbon content obtained by environmental monitoring and the expected carbon content adjustment result is calculated by comparing the two, and the formula is adopted: a deviation from the expected carbon content in real time is generated, wherein, Representing the carbon content monitored in real-time,Indicating the result of the desired carbon content adjustment,Indicating a deviation in real time from the expected carbon content,Is a stability adjustment parameter;
Comparing the deviation between the real-time and expected carbon content with a set stability threshold to determine whether adjustment is needed, using the formula: an adjustment demand flag is generated, wherein, For a deviation from the expected carbon content in real time,As a threshold value for the stability,To adjust the demand flag (if the result is greater than or equal to 1, then adjustment is required, otherwise not required),Is a weight parameter;
According to the adjustment requirement sign and the deviation between the real-time and the expected carbon content, and combining the requirement of stability analysis, adopting the formula: Generating a carbon content stability analysis result, wherein, In order to adjust the demand sign(s),For a deviation from the expected carbon content in real time,As a result of the analysis of the stability of the carbon content,Is a normalization parameter.
Deviation formula of real-time and expected carbon content:; monitoring the carbon content in real time; Expected carbon content adjustment results; Stability adjusting parameters for adjusting the sensitivity of the deviation calculation; deviation from the expected carbon content in real time;
Assuming a carbon content monitored in real time at a certain point in time Ppm, expected carbon content adjustment resultsPpm, stability adjustment parameterDeviation is thenIs calculated as follows: . This result shows that the real-time carbon content is about 4.76ppm higher than expected.
Adjusting a demand sign formula:; weight parameters, adjusting the sensitivity of threshold comparison; an adjustment requirement sign (1 indicates that adjustment is required, 0 indicates that adjustment is not required); a stability threshold; deviation from expected carbon content in real time, continuing with the example of the deviation, assuming a stability threshold Ppm, weight parameterCalculation ofThe following are provided: Due to Then an adjustment is required.
Carbon content stability analysis result formula:; adjusting a demand sign; deviation from the expected carbon content in real time; Normalizing parameters for adjusting the influence intensity of the deviation; final analysis result of carbon content stability assuming normalized parameters Calculation ofThe following are provided: . This result shows that, depending on the analysis, the effect of the actual carbon content deviation needs to be significantly adjusted, with an adjustment factor of about 6.01 reflecting the urgency and magnitude of the adjustment that needs to be made.
Referring to fig. 7, the steps for obtaining the carbon content parameter optimization scheme specifically include:
And calculating the carbon emission deviation by using the carbon content stability analysis result and combining the key adjustment coefficient of the electric furnace, and adopting the formula: a real-time carbon offset is generated, wherein, The results of the carbon content stability analysis are shown,For the adjustment of the coefficient of carbon deviation,Is the carbon deviation amount;
based on the real-time carbon deviation, the operation parameter adjustment quantity of the electric furnace is determined by adopting feedback adjustment, and the operation parameter adjustment quantity is calculated by the formula: Introducing an electric furnace performance adjustment factor and an adjustment baseline coefficient to generate an electric furnace operation parameter adjustment quantity, wherein, Is the amount of carbon deviation that is used,AndThe performance adjustment factor and the adjustment baseline coefficient are respectively,The adjustment quantity of the operation parameters of the electric furnace;
according to the adjustment quantity of the operation parameters of the electric furnace, optimizing the parameters of the electric furnace and reducing the carbon emission by an optimization algorithm, and using the formula: Generating a carbon content parameter optimization scheme, wherein, For the adjustment of the operating parameters of the electric furnace,In order to optimize the coefficient of efficiency,And optimizing the scheme for the carbon content parameter.
Carbon deviation amount formula:; analysis of carbon content stability, obtained from a carbon monitoring system, assuming values of (In ppm) representing deviations of the current carbon content beyond the intended target; Carbon deviation adjustment coefficient, which is a fixed scale coefficient set according to the equipment performance and history data, is assumed 0 Assume the current stability analysis result5.0Ppm, adjustment coefficient0.1, Then: . This result Ppm indicates the current actual carbon emissions beyond the expected amount.
An electric furnace operation parameter adjustment formula:; the amount of carbon deviation obtained from the previous step; And Performance adjustment factors and adjustment baseline coefficients for weighting the effects of carbon bias to adjust furnace operating parameters, respectively, assumingAndUsing the calculation in the previous stepppm,A kind of electronic deviceCalculating:. This resultIndicating that corresponding adjustments to the furnace are required to reduce carbon emissions to the desired level.
Carbon content parameter optimization scheme formula:; The electric furnace operation parameter adjustment amount obtained from the previous step; optimizing efficiency coefficients, representing the efficiency of the adjustment operation, assuming (Unit:%);usingthe results in the previous stepOptimizing coefficientsCalculating:. This resultIndicating that the operating parameters of the electric furnace are adjusted to a new level in order to achieve an optimised carbon emission effect.
Referring to fig. 8, the steps for obtaining the analysis result of the carbon control efficiency specifically include:
Based on the carbon content parameter optimization scheme and the current operation parameters of the electric furnace, adjusting the parameters and matching the preset carbon content level, adopting the formula: generating updated operating parameters of the electric furnace, wherein, Indicating the original operating parameters of the electric furnace,Is the coefficient of optimization and is used to determine,AndIs a new coefficient of the influence of the adjustment,The updated electric furnace operation parameters;
And evaluating the influence on the carbon emission and the energy consumption by using the updated electric furnace operation parameters, and adopting the formula: Generating adjusted energy consumption data, wherein, As the raw energy consumption data is to be used,In order to update the operating parameters of the electric furnace,AndIn order to adjust the coefficient of energy consumption,The energy consumption data after the adjustment;
according to the adjusted energy consumption data and the production index, adopting the formula: Calculating and generating a carbon control efficiency analysis result, wherein, AndRespectively the energy consumption data before and after adjustment,In order to produce an index of the efficiency of production,And represents the analysis result of the carbon control efficiency.
The updated electric furnace operation parameters are represented by the formula: original electric furnace operation parameters; the adjustment coefficients obtained from the optimization scheme are assumed to be the ratio of increasing carbon efficiency; Adjusting parameters for balancing new coefficients of the optimization influence, assuming: (this may be an initial operating parameter value for the electric furnace, such as a set point for temperature or pressure); (indicating that the optimization scheme suggests a 5% improvement in parameters); (adjustment factor, ensure optimization is not excessive); calculation: . Results Indicating that the new parameter after adjustment is 105, i.e. 5% higher than the original set point, reflecting the expected optimization effect.
The adjusted energy consumption data formula:; original energy consumption data; The operation parameters of the electric furnace after adjustment; energy consumption adjustment coefficient, presume: (unit energy consumption); (adjustment coefficient); calculation: . Results Indicating an approximate half reduction in energy consumption after adjustment, indicating that the optimization measures significantly improve energy efficiency.
And C, analyzing a result formula of the carbon control efficiency:; Respectively adjusting the energy consumption data before and after adjustment; production efficiency index, presume: Calculating :. ResultsIndicating that the production efficiency index per unit saves about 10.06 units of energy consumption, which is a significant efficiency improvement.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.

Claims (1)

1.一种基于大数据的电炉炼钢含碳量控制系统，其特征在于，所述系统包括：1. A carbon content control system for electric furnace steelmaking based on big data, characterized in that the system comprises: 碳含量初步控制模块基于历史操作数据和标准生产要求，设定电炉操作的初始条件，包括温度、时间、加料速度和初始碳含量，获取初始操作参数，根据所述初始操作参数，调整电炉的炉温控制和料速，形成初步碳控调整策略；The carbon content preliminary control module sets the initial conditions for the operation of the electric furnace, including temperature, time, feeding speed and initial carbon content, based on historical operation data and standard production requirements, obtains initial operation parameters, and adjusts the furnace temperature control and feeding speed of the electric furnace according to the initial operation parameters to form a preliminary carbon control adjustment strategy; 所述初始操作参数的获取步骤具体为：The steps for obtaining the initial operating parameters are specifically as follows: 基于历史操作数据和标准生产要求，从电炉操作数据中提取关键操作参数，包括温度、时间、加料速度和初始碳含量，整合成历史操作数据集合，对比历史操作数据集合与电炉的标准生产参数，采用公式：Based on historical operation data and standard production requirements, key operation parameters, including temperature, time, feeding speed and initial carbon content, are extracted from the electric furnace operation data and integrated into a historical operation data set. The historical operation data set is compared with the standard production parameters of the electric furnace using the formula: 计算偏差百分比，生成偏差数据集合，其中，Pj，S代表标准参数中的第j个参数，Pj，M代表历史操作数据中的第j个参数，m代表参数数量，ΔP代表参数的平均偏差百分比；Calculate the deviation percentage and generate a deviation data set, where P j,S represents the jth parameter in the standard parameters, P j,M represents the jth parameter in the historical operation data, m represents the number of parameters, and ΔP represents the average deviation percentage of the parameters; 利用所述偏差数据集合中的信息，对电炉的标准生产参数进行动态调整，应用公式：Using the information in the deviation data set, the standard production parameters of the electric furnace are dynamically adjusted, applying the formula: Sadj＝S·(1-ka·ΔP) Sadj = S·(1- ka ·ΔP) 计算并得到调整后的操作参数集合，其中，S代表未调整的标准参数，Sadi代表调整后的参数，ka代表基于偏差的调整系数，ΔP代表参数的平均偏差百分比；Calculate and obtain an adjusted set of operating parameters, where S represents an unadjusted standard parameter, Sadi represents an adjusted parameter, ka represents an adjustment coefficient based on deviation, and ΔP represents an average deviation percentage of the parameter; 结合所述调整后的操作参数集合与当前的生产需求和设备状态，应用公式：Combining the adjusted set of operating parameters with the current production demand and equipment status, apply the formula: 生成初始操作参数，其中，Pinit代表初始操作参数，α和β是调整因子，γ为正则化常数，Sadj代表调整后的参数；Generate initial operating parameters, where P init represents the initial operating parameters, α and β are adjustment factors, γ is the regularization constant, and S adj represents the adjusted parameters; 所述初步碳控调整策略的获取步骤具体为：The steps for obtaining the preliminary carbon control adjustment strategy are as follows: 基于所述初始操作参数，捕捉原始炉温和料速，根据材料反应系数和调节系数，采用公式：Based on the initial operating parameters, the original furnace temperature and material rate are captured, and according to the material reaction coefficient and adjustment coefficient, the formula is used: 计算并得到当前碳浓度结果，其中，Tb代表当前炉温，Vb代表料速，Rb为材料反应系数，k为调节系数，Cb为当前碳浓度；Calculate and obtain the current carbon concentration result, where Tb represents the current furnace temperature, Vb represents the material speed, Rb is the material reaction coefficient, k is the adjustment coefficient, and Cb is the current carbon concentration; 对比当前碳浓度结果与目标碳控水平，计算调整需求百分比，采用公式Compare the current carbon concentration results with the target carbon control level and calculate the adjustment requirement percentage using the formula 生成碳控调整需求，其中，Ctarget代表目标碳控水平，Cb为当前碳浓度，ΔCb为调整需求百分比，Pb为修正因子；Generate carbon control adjustment demand, where C target represents the target carbon control level, C b is the current carbon concentration, ΔC b is the adjustment demand percentage, and P b is the correction factor; 根据碳控调整需求，调整炉温和料速，采用公式：According to the carbon control adjustment requirements, adjust the furnace temperature and material speed, using the formula: 和and 生成初步碳控调整策略，其中，Tnew为新炉温设定，vnew为新料速设定，Tb代表当前炉温，Vb代表料速，ΔCb为调整需求百分比；Generate a preliminary carbon control adjustment strategy, where T new is the new furnace temperature setting, v new is the new material rate setting, T b represents the current furnace temperature, V b represents the material rate, and ΔC b is the adjustment demand percentage; 碳含量动态调整模块基于所述初步碳控调整策略，对炉温控制和料速进行实时调整，获取实时调整数据，通过所述实时调整数据计算调整后的碳含量预期变化，生成预期碳含量调整结果；The carbon content dynamic adjustment module adjusts the furnace temperature control and material speed in real time based on the preliminary carbon control adjustment strategy, obtains real-time adjustment data, calculates the expected change of carbon content after adjustment through the real-time adjustment data, and generates an expected carbon content adjustment result; 所述实时调整数据的获取步骤具体为：The steps of acquiring the real-time adjustment data are specifically as follows: 基于所述初步碳控调整策略，实时测量炉内温度，通过温度传感器记录，将温度数据转化为控制输入，采用公式：Based on the preliminary carbon control adjustment strategy, the furnace temperature is measured in real time and recorded by the temperature sensor. The temperature data is converted into control input using the formula: 生成温度控制参数，其中，Tq代表当前测得的炉温，r为温度调整系数，p为衰减因子，Tmax为设备最大可承受温度，Tc代表温度控制参数；Generate temperature control parameters, where Tq represents the currently measured furnace temperature, r is the temperature adjustment coefficient, p is the attenuation factor, Tmax is the maximum tolerable temperature of the equipment, and Tc represents the temperature control parameter; 根据所述温度控制参数，调整料速，采用公式：According to the temperature control parameters, adjust the material speed, using the formula: 得到料速控制参数，其中，Vbase代表基准料速，Tset代表设定温度，kc代表料速调整因子，Vc代表料速控制参数，Tc代表温度控制参数；The material speed control parameters are obtained, wherein V base represents the base material speed, T set represents the set temperature, k c represents the material speed adjustment factor, V c represents the material speed control parameter, and Tc represents the temperature control parameter; 结合温度控制参数和料速控制参数，采集当前炉内温度和料速信息，将数据传输至控制单元，分析温度数据和料速数据的均值和偏差，调整电炉的温度和料速设置，采用公式：Combined with the temperature control parameters and material speed control parameters, the current furnace temperature and material speed information is collected, the data is transmitted to the control unit, the mean and deviation of the temperature data and material speed data are analyzed, and the temperature and material speed settings of the electric furnace are adjusted using the formula: 获取实时调整数据，其中，Vc代表料速控制参数，Tc代表温度控制参数，Rc代表实时调整数据；Acquire real-time adjustment data, where V c represents the material speed control parameter, Tc represents the temperature control parameter, and R c represents the real-time adjustment data; 所述预期碳含量调整结果的获取步骤具体为：The steps for obtaining the expected carbon content adjustment result are specifically as follows: 基于所述实时调整数据，通过环境监测传感器定时收集实时碳含量数据，利用数据清洗算法移除噪声和异常值，根据公式：Based on the real-time adjustment data, the real-time carbon content data is collected regularly through the environmental monitoring sensor, and the noise and outliers are removed by the data cleaning algorithm according to the formula: 得到实时碳数据集，其中，Ci，d代表每个数据点的碳含量，n代表总数据点数，μd代表数据点的平均值，Cclean，d代表经过处理的数据集；A real-time carbon data set is obtained, where Ci ,d represents the carbon content of each data point, n represents the total number of data points, μd represents the average value of the data points, and Cclean ,d represents the processed data set; 基于所述实时碳数据集和环境变化系数，使用公式：Based on the real-time carbon data set and the environmental change coefficient, the formula is used: 计算并得到预期碳含量变化量，其中，βd代表环境变化影响系数，Cclean，d代表经过处理的数据集，代表预期碳含量变化量；Calculate and obtain the expected carbon content change, where β d represents the environmental change impact coefficient, C clean, d represents the processed data set, and represents the expected carbon content change; 根据所述预期碳含量变化量和政策设定的环境阈值，利用公式：According to the expected carbon content change and the environmental threshold set by the policy, the formula is used: 得到预期碳含量调整结果，其中，ΔCexp，d代表预期碳含量变化量，θd代表环境政策阈值，Cadj，d代表调整后的碳含量结果，Cclean，d代表经过处理的数据集；The expected carbon content adjustment result is obtained, where ΔC exp,d represents the expected carbon content change, θ d represents the environmental policy threshold, C adj,d represents the adjusted carbon content result, and C clean,d represents the processed data set; 碳含量监控与反馈模块利用所述预期碳含量调整结果，监测实时碳含量与预期碳含量的偏差，生成碳含量稳定性分析结果，基于所述碳含量稳定性分析结果，调整电炉运行参数并优化碳含量监控，生成碳含量参数优化方案；The carbon content monitoring and feedback module uses the expected carbon content adjustment result to monitor the deviation between the real-time carbon content and the expected carbon content, generates a carbon content stability analysis result, adjusts the electric furnace operation parameters and optimizes the carbon content monitoring based on the carbon content stability analysis result, and generates a carbon content parameter optimization plan; 所述碳含量稳定性分析结果的获取步骤具体为：The steps for obtaining the carbon content stability analysis result are specifically as follows: 基于所述预期碳含量调整结果，通过将环境监测获取的实时碳含量与预期碳含量调整结果进行比较，计算两者的偏差，采用公式：Based on the expected carbon content adjustment result, the real-time carbon content obtained by environmental monitoring is compared with the expected carbon content adjustment result to calculate the deviation between the two, using the formula: 生成实时与预期碳含量的偏差，其中，Qe表示实时监测的碳含量，Be表示预期碳含量调整结果，ΔPe表示实时与预期碳含量的偏差，∈e是稳定性调节参数；Generate the deviation between the real-time and expected carbon content, where Q e represents the real-time monitored carbon content, Be represents the expected carbon content adjustment result, ΔP e represents the deviation between the real-time and expected carbon content, and ∈ e is the stability adjustment parameter; 利用所述实时与预期碳含量的偏差与设定的稳定性阈值进行比较，决定是否需要进行调整，使用公式：The deviation between the real-time and expected carbon content is compared with the set stability threshold to determine whether adjustment is needed, using the formula: 生成调整需求标志，其中，ΔPe为实时与预期碳含量的偏差，Te为稳定性阈值，Ae为调整需求标志，若结果大于等于1，则需要调整，否则不需要，γe是权重参数；Generate an adjustment requirement flag, where ΔP e is the deviation between the real-time and expected carbon content, T e is the stability threshold, A e is the adjustment requirement flag, if the result is greater than or equal to 1, adjustment is required, otherwise no adjustment is required, and γ e is the weight parameter; 根据所述调整需求标志和实时与预期碳含量的偏差，结合稳定性分析的要求，采用公式：According to the adjustment requirement flag and the deviation between the real-time and expected carbon content, combined with the requirements of stability analysis, the formula is adopted: 生成碳含量稳定性分析结果，其中，Ae为调整需求标志，ΔPe为实时与预期碳含量的偏差，Se为碳含量稳定性分析结果，λe是归一化参数；Generate a carbon content stability analysis result, where Ae is an adjustment demand flag, ΔPe is a deviation between the real-time and expected carbon content, Se is a carbon content stability analysis result, and λe is a normalization parameter; 所述碳含量参数优化方案的获取步骤具体为：The steps for obtaining the carbon content parameter optimization scheme are specifically as follows: 利用所述碳含量稳定性分析结果，结合电炉关键的调节系数，计算碳排放偏差量，采用公式：The carbon content stability analysis results are used in combination with the key adjustment coefficient of the electric furnace to calculate the carbon emission deviation using the formula: ΔCh＝Se·kh ΔC h = Se · k h 生成实时碳偏差量，其中，Se表示碳含量稳定性分析结果，kh为碳偏差调节系数，ΔCh为碳偏差量；Generate real-time carbon deviation, where Se represents the carbon content stability analysis result, kh is the carbon deviation adjustment coefficient, and ΔCh is the carbon deviation; 基于所述实时碳偏差量，采用反馈调整确定电炉的运行参数调整量，通过公式：Based on the real-time carbon deviation, feedback adjustment is used to determine the adjustment amount of the operation parameters of the electric furnace, through the formula: 引入电炉性能调整因子和调整基线系数，生成电炉运行参数调整量，其中，ΔCh为碳偏差量，ah和bh分别为性能调整因子和调整基线系数，Ph为电炉运行参数调整量；The electric furnace performance adjustment factor and adjustment baseline coefficient are introduced to generate the electric furnace operation parameter adjustment amount, where ΔC h is the carbon deviation, a h and b h are the performance adjustment factor and adjustment baseline coefficient respectively, and Ph is the electric furnace operation parameter adjustment amount; 根据所述电炉运行参数调整量，通过优化算法，优化电炉参数并降低碳排放量，使用公式：According to the adjustment amount of the electric furnace operating parameters, the electric furnace parameters are optimized and the carbon emissions are reduced through the optimization algorithm, using the formula: 生成碳含量参数优化方案，其中，Ph为电炉运行参数调整量，dh为优化效率系数，Oh为碳含量参数优化方案；Generate a carbon content parameter optimization plan, where Ph is the adjustment amount of the electric furnace operation parameters, dh is the optimization efficiency coefficient, and Oh is the carbon content parameter optimization plan; 电炉效率优化模块根据所述碳含量参数优化方案，调整电炉的操作参数，匹配预定的碳含量水平，评估参数调整的影响，对比调整前后的能耗数据，记录关键生产指标的变化，生成碳控效率分析结果；The electric furnace efficiency optimization module adjusts the operating parameters of the electric furnace according to the carbon content parameter optimization scheme to match the predetermined carbon content level, evaluates the impact of the parameter adjustment, compares the energy consumption data before and after the adjustment, records the changes in key production indicators, and generates carbon control efficiency analysis results; 所述碳控效率分析结果的获取步骤具体为：The steps for obtaining the carbon control efficiency analysis results are specifically as follows: 基于所述碳含量参数优化方案和当前电炉的运行参数，调整参数并匹配预定的碳含量水平，采用公式：Based on the carbon content parameter optimization scheme and the current operating parameters of the electric furnace, the parameters are adjusted to match the predetermined carbon content level using the formula: 生成更新后的电炉运行参数，其中，Poldg表示原始电炉运行参数，Og是优化系数，Vg和Wg是调整影响的新系数，Pg是更新后的电炉运行参数；Generate updated electric furnace operating parameters, where P oldg represents the original electric furnace operating parameters, O g is the optimization coefficient, V g and W g are the new coefficients of adjustment influence, and P g is the updated electric furnace operating parameters; 利用所述更新后的电炉运行参数，评估对碳排放和能耗的影响，采用公式：Using the updated electric furnace operating parameters, the impact on carbon emissions and energy consumption is evaluated using the formula: 生成调整后的能耗数据，其中，Eoldg为原始能耗数据，Pg为更新后的电炉运行参数，Xg和Yg为能耗调节系数，Eg为调整后的能耗数据；Generate adjusted energy consumption data, where E oldg is the original energy consumption data, P g is the updated electric furnace operation parameter, X g and Y g are energy consumption adjustment coefficients, and E g is the adjusted energy consumption data; 根据所述调整后的能耗数据和生产指标，采用公式：According to the adjusted energy consumption data and production indicators, the formula is adopted: 计算并生成碳控效率分析结果，其中，Eoldg和Eg分别为调整前后的能耗数据，Zg为生产效率指数，Cg表示碳控效率分析结果。Calculate and generate the carbon control efficiency analysis results, where E oldg and E g are the energy consumption data before and after adjustment, Z g is the production efficiency index, and C g represents the carbon control efficiency analysis results.
CN202411300972.2A 2024-09-18 2024-09-18 Big data-based electric furnace steelmaking carbon content control system Active CN118813903B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202411300972.2A CN118813903B (en) 2024-09-18 2024-09-18 Big data-based electric furnace steelmaking carbon content control system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202411300972.2A CN118813903B (en) 2024-09-18 2024-09-18 Big data-based electric furnace steelmaking carbon content control system

Publications (2)

Publication Number Publication Date
CN118813903A CN118813903A (en) 2024-10-22
CN118813903B true CN118813903B (en) 2024-12-03

Family

ID=93068354

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202411300972.2A Active CN118813903B (en) 2024-09-18 2024-09-18 Big data-based electric furnace steelmaking carbon content control system

Country Status (1)

Country Link
CN (1) CN118813903B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119026065B (en) * 2024-10-29 2025-01-24 四川德润钢铁集团航达钢铁有限责任公司 A method for fault diagnosis of short-process electric furnace steelmaking equipment
CN119620814B (en) * 2025-02-13 2025-05-16 北京春风药业有限公司 Intelligent control system is drawed to chinese-medicinal material

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2894813A1 (en) * 2012-12-21 2014-06-26 Sms Siemag Ag Method and device for predicting, controlling and/or regulating steelworks processes
CA3053311A1 (en) * 2019-06-17 2020-12-17 Billy W. Bryant Electric arc furnace, system and method

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105974896B (en) * 2016-06-07 2018-08-28 东北大学 A kind of pneumatic steelmaking Optimal Control System and method based on information physical fusion
CN110885912B (en) * 2019-11-18 2021-10-15 中冶赛迪上海工程技术有限公司 Automatic steelmaking method and system based on data analysis
CN117055509B (en) * 2023-09-25 2024-03-08 四川德润钢铁集团航达钢铁有限责任公司 Method for predicting short-process steel process parameters based on artificial intelligence
CN118011806A (en) * 2024-01-25 2024-05-10 长春蓝拓科技有限公司 Intelligent steelmaking end point carbon temperature dynamic control method and system based on PSO-BP neural network
CN117930786B (en) * 2024-03-21 2024-06-11 山东星科智能科技股份有限公司 Intelligent digital twin simulation system for steel production process

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2894813A1 (en) * 2012-12-21 2014-06-26 Sms Siemag Ag Method and device for predicting, controlling and/or regulating steelworks processes
CA3053311A1 (en) * 2019-06-17 2020-12-17 Billy W. Bryant Electric arc furnace, system and method

Also Published As

Publication number Publication date
CN118813903A (en) 2024-10-22

Similar Documents

Publication Publication Date Title
CN118813903B (en) Big data-based electric furnace steelmaking carbon content control system
CN118938842B (en) Big data-based electric furnace production management system
CN118131079B (en) Megasonic power supply software control method and system
CN119247781A (en) Building energy consumption and carbon emission dual control system based on BIM
CN112914139A (en) Method and system for controlling water adding amount in loosening and moisture regaining process
CN118504930B (en) Decarburization electric control real-time data analysis system
CN119806254A (en) Automated heater monitoring system, method and device for vacuum isothermal forging
CN115601313A (en) Visual monitoring management system for tempered glass production process
CN110295338A (en) A kind of stable strip enters the control method of zinc pot temperature
CN115584371A (en) Design method and application of blast furnace temperature closed-loop control system
CN120255595A (en) A method and system for controlling the basicity of sintered ore
CN119310953B (en) Recycled gas control system suitable for thermal intensity control of coke production
CN118616979A (en) A dynamic control system and method for welding process parameters of large pressure vessels
CN117047021A (en) Intelligent carbon emission reduction methods and systems
CN120928858B (en) A flow control system and method for furnace equipment
CN102534138A (en) Oxygen potential control system of spheroidizing annealing furnace
CN119101780B (en) An ultra-low emission intelligent control method for electric furnace steelmaking
CN114042762B (en) Production method for improving rust resistance of deformed steel bar
CN118761022B (en) A cooling tower regulation system based on Internet of Things interaction
CN111286570A (en) Method for regulating and controlling abnormal operation furnace type by using scanning radar
CN119082823B (en) Hardware surface oxidation treatment process and system
TW202404719A (en) Furnace monitoring system and method thereof
CN118854049B (en) Electric heating heat treatment trolley furnace control system
CN220624832U (en) Automatic control system of lime rotary kiln
CN120928858A (en) A flow control system and method for furnace equipment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant